Check correlation strength among different attribute

Visualise the scatter plot to check collinearity anomg pair of attributes

Find missing value and find 1440 data point is missing in load attributes

Visualise the missing value in heatmap pattern and find few few data point is missing in load variable

Missing datapoint in load variale with its count and datetime

Missing datapoint with date and timespan

Modify our dataframe for exploratory data analysis and finds some insights and as time span is very short which is 15 miniutes and ery less variation in datapoint in short span so we convert it to 1 hour for better visualisation and take the mean of corresponding attributes

We change timespan of 15 minutes to 1 hours to decrese datapoint for better visualization and insights

Descriptive Analysis of dataset of load variable

Plot Each atrributes in time series scale and find no trend but cyclicity and seasonility

Check daily fluctuation of load and find some pattern in day and night like peak at afternoon

Annual mean demand of three year and find there is no trend as mean is increases yhen decreases

Quaterly mean demand of of each year and find there is seasonilty in every year

There is not trend yearly as visualize in plot

Clearly in quaterly plot seasonility in dataset maximum demand in nealy july month in every year